Enhancing Personalized Book Recommender System.

Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computesdocument similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a low prediction rate for large vocabulary. In this study, we present an algorithm that utilises Euclidean distance similarity function, to solve the identified problem. Th e performance of the proposed scheme was evaluated against the benchmark scheme usingdifferent performance metrics. The proposed scheme was implemented and an experimentally tested by using the benchmark datasets (Amazon review datasets). The results revealed that, the proposed scheme achieved better performance than the existing recommender system in terms of Root Mean Square Error (RMSE) which reduces the errors by 29% and also increase the Precision and Recall by 51.4%, and 55.8% respectively in the 1 million datasets.